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Research on PCA-GA-ELM Model of Mine Inflow Prediction

LIU Zhixiang，LIU Yiran，LAN Ming   

  1. School of  Resources and Safety Engineering，Central South University，Changsha   410083，Hunan，China
  • Received:2016-06-30 Revised:2016-11-14 Online:2017-02-28 Published:2017-05-12

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Abstract:

In order to predict mine inflow more rapidly and effectively，and improve the prediction accuracy，a new method combining principal component analysis（PCA），genetic algorithm（GA） and extreme learning machine（ELM） for mine inflow prediction was proposed based on the analyses of mine inflow influence factors.According to the engineering example，9 main factors were selected as the prediction indexes，and PCA was used to reduce data dimension.Considering the disadvantage of ELM，GA was used to optimize the related parameters of ELM，and then PCA-GA-ELM model of mine inflow prediction was built.Then the model was trained and tested，and the prediction results of PCA-GA-ELM，GA-ELM and ELM model were comparatively analyzed.The prediction results fit better than the other two models with the actual situation.The PCA-GA-ELM model was superior to GA-ELM model and ELM，and it can be effectively applied to mine inflow prediction，which provided scientific references and guidance in mining production.

Key words: mine inflow, principal component analysis, genetic algorithm, ELM model, prediction

CLC Number: 

  • X935

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